Bài giảng Chapter 4: Light and color capture

Lecture with the content review of lighting – color, reflection and absorption, image represented, color spaces... For details of knowledge, please refer to the lecture.

Chapter 4 Light and Color Capture

James Hays, Brown University

Department of Mechatronics

Contents

• Review of lighting

– Color, Reflection, and absorption

• What is a pixel? How is an image represented?

– Color spaces

A photon’s life choices

λ

A photon’s life choices

λ

λ

A photon’s life choices

λ

λ

A photon’s life choices

Fluorescent scorpion Image courtesy of The Firefly Forest.

A photon’s life choices

t=1 light source

t=n

Phosphorescence is a related type of photoluminescence

in which absorbed radiation is re-emitted more slowly, so phosphorescent objects can still glow for periods up to several hours after the source of incident radiation is removed.

• Your eyes work a lot like a camera The lens of a camera

focuses light onto the film inside The cornea and lens in the front of the eye focus light onto the back, where light- sensitive tissue called the retina is located When the retina receives an image, it sends a signal through the optic nerve

to the brain for the image to be developed.

• http://www.healthline.com/vpvideo/vision

The Eye

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The Eye

The human eye is a camera!

• Iris - colored annulus with radial muscles

• Pupil - the hole (aperture) whose size is controlled by the iris

• What’s the “film”?

– photoreceptor cells (rods and cones) in the retina

Slide by Steve Seitz

The Retina

Cross-section of eye

Ganglion axons

Ganglion cell layer

Bipolar cell layer

Receptor layer

Pigmented epithelium Cross section of retina

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What humans don’t have: tapetum lucidum

C on es cone-shaped less sensitive operate in high light color vision

Two types of light-sensitive receptors

cone

rod

Rods rod-shaped highly sensitive operate

at night gray-scale vision

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Rod / Cone sensitivity

The famous sock-matching problem…

Electromagnetic Spectrum

Human Luminance Sensitivity Function

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Measuring spectra

Spectroradiometer: separate input light into its different wavelengths, and measure the energy at each.

The Physics of Light

Any patch of light can be completely described

physically by its spectrum: the number of photons (per time unit) at each wavelength 400 - 700 nm.

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© Stephen E Palmer, 2002

The Physics of Light

Some examples of the reflectance spectra of surfaces

Image Formation

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Digital camera

A digital camera replaces film with a sensor array

• Each cell in the array is light-sensitive diode that converts photons

to electrons

• http://electronics.howstuffworks.com/digital-camera.htm

Sensor Array

CMOS sensor

CCD sensor

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Interlace vs progressive scan

http://www.axis.com/products/video/camera/progressive_scan.htm

What is an image?

color of the image at that point.

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Digital Images and Pixels

• A digital image is the representation of a continuous image f(x,y) by a 2-d array of discrete samples f[x,y] The amplitude of each sample is quantized to be represented by a finite number of bits.

• Each element of the 2-d array of samples is called a pixel or

pel (from "picture element")

• Think of pixels as point samples, without extent.

Image Resolution

These images were produced by simply picking every n-th sample horizontally and vertically and replicating that value nxn times.

We can do better

• Pre-filtering before subsampling to avoid aliasing

• Smooth interpolation

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The raster image (pixel matrix)

The raster image (pixel matrix)

0.92 0.93 0.94 0.97 0.62 0.37 0.85 0.97 0.93 0.92 0.99 0.95 0.89 0.82 0.89 0.56 0.31 0.75 0.92 0.81 0.95 0.91 0.89 0.72 0.51 0.55 0.51 0.42 0.57 0.41 0.49 0.91 0.92 0.96 0.95 0.88 0.94 0.56 0.46 0.91 0.87 0.90 0.97 0.95 0.71 0.81 0.81 0.87 0.57 0.37 0.80 0.88 0.89 0.79 0.85 0.49 0.62 0.60 0.58 0.50 0.60 0.58 0.50 0.61 0.45 0.33 0.86 0.84 0.74 0.58 0.51 0.39 0.73 0.92 0.91 0.49 0.74 0.96 0.67 0.54 0.85 0.48 0.37 0.88 0.90 0.94 0.82 0.93 0.69 0.49 0.56 0.66 0.43 0.42 0.77 0.73 0.71 0.90 0.99 0.79 0.73 0.90 0.67 0.33 0.61 0.69 0.79 0.73 0.93 0.97 0.91 0.94 0.89 0.49 0.41 0.78 0.78 0.77 0.89 0.99 0.93

Examples of additive color systems

C R T p h osp h ors Mult i p le p r o j ectors

Some common optical illusions

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Optical illusions

Try to count the number of black dots

on the image below

Are the lines below straight or are they curved?

Optical illusions

It's a spiral, right?

How many legs does this elephant have?

Images in Matlab

• Images represented as a matrix

• Suppose we have a NxM RGB image called “im”

– im(1,1,1) = top-left pixel value in R-channel

– im(y, x, b) = y pixels down, x pixels to right in the bthchannel

– im(N, M, 3) = bottom-right pixel in B-channel

• imread(filename) returns a uint8 image (values 0 to 255)

– Convert to double format (values 0 to 1) with im2double

0.92 0.93 0.94 0.97 0.62 0.37 0.85 0.97 0.93 0.92 0.99 0.95 0.89 0.82 0.89 0.56 0.31 0.75 0.92 0.81 0.95 0.91 0.89 0.72 0.51 0.55 0.51 0.42 0.57 0.41 0.49 0.91 0.92 0.96 0.95 0.88 0.94 0.56 0.46 0.91 0.87 0.90 0.97 0.95 0.71 0.81 0.81 0.87 0.57 0.37 0.80 0.88 0.89 0.79 0.85 0.49 0.62 0.60 0.58 0.50 0.60 0.58 0.50 0.61 0.45 0.33 0.86 0.84 0.74 0.58 0.51 0.39 0.73 0.92 0.91 0.49 0.74 0.96 0.67 0.54 0.85 0.48 0.37 0.88 0.90 0.94 0.82 0.93 0.69 0.49 0.56 0.66 0.43 0.42 0.77 0.73 0.71 0.90 0.99 0.79 0.73 0.90 0.67 0.33 0.61 0.69 0.79 0.73 0.93 0.97

0.92 0.93 0.94 0.97 0.62 0.37 0.85 0.97 0.93 0.92 0.99 0.95 0.89 0.82 0.89 0.56 0.31 0.75 0.92 0.81 0.95 0.91 0.89 0.72 0.51 0.55 0.51 0.42 0.57 0.41 0.49 0.91 0.92 0.96 0.95 0.88 0.94 0.56 0.46 0.91 0.87 0.90 0.97 0.95 0.71 0.81 0.81 0.87 0.57 0.37 0.80 0.88 0.89 0.79 0.85 0.49 0.62 0.60 0.58 0.50 0.60 0.58 0.50 0.61 0.45 0.33 0.86 0.84 0.74 0.58 0.51 0.39 0.73 0.92 0.91 0.49 0.74 0.96 0.67 0.54 0.85 0.48 0.37 0.88 0.90 0.94 0.82 0.93 0.69 0.49 0.56 0.66 0.43 0.42 0.77 0.73 0.71 0.90 0.99 0.79 0.73 0.90 0.67 0.33 0.61 0.69 0.79 0.73 0.93 0.97 0.91 0.94 0.89 0.49 0.41 0.78 0.78 0.77 0.89 0.99 0.93

0.97 0.62 0.37 0.85 0.97 0.93 0.92 0.99 0.82 0.89 0.56 0.31 0.75 0.92 0.81 0.95 0.91 0.51 0.55 0.51 0.42 0.57 0.41 0.49 0.91 0.92 0.88 0.94 0.56 0.46 0.91 0.87 0.90 0.97 0.95 0.81 0.87 0.57 0.37 0.80 0.88 0.89 0.79 0.85 0.60 0.58 0.50 0.60 0.58 0.50 0.61 0.45 0.33 0.74 0.58 0.51 0.39 0.73 0.92 0.91 0.49 0.74 0.54 0.85 0.48 0.37 0.88 0.90 0.94 0.82 0.93 0.56 0.66 0.43 0.42 0.77 0.73 0.71 0.90 0.99 0.90 0.67 0.33 0.61 0.69 0.79 0.73 0.93 0.97 0.89 0.49 0.41 0.78 0.78 0.77 0.89 0.99 0.93

G

B

row column

0.92 0.93 0.95 0.89 0.89 0.72 0.96 0.95 0.71 0.81 0.49 0.62 0.86 0.84 0.96 0.67 0.69 0.49 0.79 0.73 0.91 0.94

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Color spaces

• How can we represent color?

http://en.wikipedia.org/wiki/File:RGB_illumination.jpg

Color spaces: RGB

Default color space

0,1,0

0,0,1 1,0,0

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Color spaces: RGB and CMY Models

• RGB color model is used in computer graphics

• M agenta (red plus blue), C yan (green plus blue), and

Y ellow (red plus green)

• The CMY color model is a subset of the RGB model and is

primarily used in color print production

YUV Color Model

• The YUV color model is the basic color model used in

analogue color TV broadcasting.

• It comprises the luminance (Y) and two color difference (U, V) components The luminance can be computed as a weighted sum of red, green and blue components; the color difference, or

chrominance , components are formed by subtracting luminance from blue and from red.

RGB Colors Cube in the YUV Color Space

• Fast to compute, good for

compression The YCbCr color space is

used for component digital video and

was developed as part of the ITU-R

BT.601

• Recommendation YCbCr is a scaled

and offset version of the YUV color space.

rgbmap = ycbcr2rgb(ycbcrmap) RGB = ycbcr2rgb(YCBCR)

RGB Colors Cube in the YCbCr Space

PhotoYCC Color Model

• The Kodak* PhotoYCC* was developed for encoding

Photo CD* image data.

RGB Colors in the YCC Color Space

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YCoCg Color Models

• The YCoCg color model was developed to increase

the effectiveness of the image compression

RGB Color Cube in the YCoCg Color Space

HSV, and HLS Color Models

• The HLS (hue, lightness, saturation) and HSV (hue,

saturation, value) color models were developed to be more

“intuitive” in manipulating with color and were designed to approximate the way humans perceive and interpret color.

HSV Solid HLS Solid

Color spaces: L*a*b*

“Perceptually uniform”* color space

•Color of foods is usually measured in units L*a*b* which is

an international standard for color measurements, adopted

by the CIE (Commission Internationale d'Eclairage).

•The lightness ranges between 0 and 100 while chromatic

parameters (a, b) range between -120 and 120.

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If you had to choose, would you rather go

without luminance or chrominance ?

Most information in intensity

Only color shown – constant intensity

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Only intensity shown – constant color

Most information in intensity

Original image

Most information in intensity